healthiar in RStudio 1/3

RStudio startup screen

healthiar in RStudio 2/3

Post installation, you can access the healthiar package landing page in RStudio by going to the Packages tab and then clicking on the healthiar package.

healthiar in RStudio 3/3

Landing page of the healthiar package in RStudio, where you find the package vignettes and function documentation.

Vignette

The vignette introduces healthiar step-by-step and contains (reproducible) examples. You can open the intro_to_healthiar vignette within RStudio or as a HTML within your browser.

Function documentation (1/) - Overview

Access the function documentation (= fun doc) by clicking on a function name in the package landing page.

Function documentation (1/) - Title

Title section

The title summarizes the function of the function (hehe) in one sentence.

The title shows up next to the function name in the package landing page.

Function documentation (1/) - Description

Description section provides additional details about the function’s purpose

Function documentation (1/) - Usage

Usage section In the usage section you can find a bare-minimum function “template”, which can either be auto-generated or created manually, as in this case.

Function documentation (1/) - Arguments

Arguments section This is the core section of the function documentation, where input type (numeric vs. string) & input options (if available) are specified.

Function documentation (1/) - Details

::: callout-warning Depending on the function, this section might be not very developed at the moment. Sometimes more function details are found in the intro vignette. :::

Function documentation (1/) - Value

Value section Information about the function output

Function documentation (1/) - Examples

Example section (optional) Shows how the function works

Function documentation (1/) - Examples output

Example output Obtained by clicking on Run examples (see previous slide)

Workflow BoD

note: quickly introduce the main workflow of burden assessments

Example: attribute call without input uncertainties

Goal: attribute COPD cases to air pollution

results_pm_copd <-
  healthiar::attribute_health(
    erf_shape = "log_linear", # Alternatives: "linear", "log_log"
    rr_central = 1.369, 
    rr_increment = 10,  # μg / m^3
    exp_central = 8.85, # μg / m^3
    cutoff_central = 5, # μg / m^3
    bhd_central = 30747 # baseline health data: COPD incidence
  ) 

results_pm_copd <-
  healthiar::attribute_health(
    erf_shape = "log_linear",
    rr_central = exdat_pm_copd$relative_risk, 
    rr_lower = exdat_pm_copd$relative_risk_lower,
    rr_upper = exdat_pm_copd$relative_risk_upper,
    rr_increment = 10, 
    exp_central = exdat_pm_copd$mean_concentration,
    cutoff_central = exdat_pm_copd$cut_off_value,
    bhd_central = exdat_pm_copd$incidents_per_100_000_per_year/1E5*exdat_pm_copd$population_at_risk,
    # bhd_central = exdat_pm_copd$incidence # Uncomment once change committed to main
  ) 

Output structure

Every attribute output consists of two lists (“folders”)

• health_main contains the main results

• health_detailed detailed results (and in some cases even more information about the assessment/calculation)

note: add here that format is tibble (but can be changed to data frame)

note: screenshot of how to click on variable in env (put that as first option) . . .

Different ways to access the results

Let’s check the main output!

results_pm_copd[["health_main"]]

Example: attribute with input uncertainties

Goal: attribute lung cancer deaths to PM2.5 exposure

results_pm_copd <-
  healthiar::attribute_health(
    erf_shape = "log_linear",
    rr_central = 1.369, 
    rr_lower = 1.124, 
    rr_upper = 1.664,
    rr_increment = 10, 
    exp_central = 8.85, 
    exp_lower = 8, 
    exp_upper = 10,
    cutoff_central = 5,
    bhd_central = 30747, 
    bhd_lower = 28000, 
    bhd_upper = 32000
  ) 

Let’s check the detailed output!

results_pm_copd[["health_detailed"]][["raw"]]

Example: attribute with exposure categories (noise)

Goal: attribute cases of high annoyance (HA) to noise exposure

exdat_noise_ha <-
  exdat_noise_ha |>
  dplyr::filter(!is.na(exdat_noise_ha$exposure_mean))

results_noise_ha <- 
  healthiar::attribute_health(
    approach_risk = "absolute_risk",
    exp_central = c(57.5, 62.5, 67.5, 72.5, 77.5),
    population = sum(exdat_noise_ha$population_exposed_total), # TO DO: hard code input here
    prop_pop_exp = exdat_noise_ha$population_exposed_total / 
      sum(exdat_noise_ha$population_exposed_total),
    erf_eq_central = "78.9270-3.1162*c+0.0342*c^2")

results_noise_ha[["health_detailed"]][["raw"]] |> knitr::kable()

Iteration example (1/)

Goal: attribute disease cases to PM2.5 exposure in multiple geographic units, such as municipalities, provinces, countries, …

results_iteration <- healthiar::attribute_health(
    geo_id_disaggregated = c("Zurich", "Basel", "Geneva", "Ticino", "Valais"), 
    geo_id_aggregated = c("Ger","Ger","Fra","Ita","Fra"),
    rr_central = 1.369,
    rr_increment = 10, 
    cutoff_central = 5,
    erf_shape = "log_linear",
    exp_central = as.list(c(11, 11, 10, 8, 7)),
    bhd_central = as.list(c(4000, 2500, 3000, 1500, 500))
)

results_iteration <- healthiar::attribute_health(
    geo_id_disaggregated = c("Zurich", "Basel", "Geneva", "Ticino", "Valais"), 
    geo_id_aggregated = c("Ger","Ger","Fra","Ita","Fra"),
    rr_central = 1.369,
    rr_increment = 10, 
    cutoff_central = 5,
    erf_shape = "log_linear",
    exp_central = c(11, 11, 10, 8, 7) |> as.list(),
    bhd_central = c(4000, 2500, 3000, 1500, 500) |> as.list()
)

Let’s check the iteration output! (/)

results_iteration[["health_main"]]

Iteration example with uncertainties

Analogously to the single geo unit example the iteration can also be run with uncertainties in one or more input variables.

results_iteration <- healthiar::attribute_health(
    geo_id_disaggregated = c("Zurich", "Basel", "Geneva", "Ticino", "Valais"), 
    geo_id_aggregated = c("Ger","Ger","Fra","Ita","Fra"),
    rr_central = 1.369, 
    rr_lower = 1.124,
    rr_upper = 1.664,
    rr_increment = 10, 
    cutoff_central = 5,
    erf_shape = "log_linear",
    exp_central = as.list(c(11, 11, 10, 8, 7)),
    exp_lower = as.list(c(10, 10, 9, 7, 6)),
    exp_upper = as.list(c(12, 12, 11, 9, 8)),
    bhd_central = as.list(c(4000, 2500, 3000, 1500, 500)),
    bhd_lower = as.list(c(3000, 1875, 2250, 1125, 375)),
    bhd_upper = as.list(c(5000, 3125, 3750, 1875, 625))
)

results_iteration[["health_detailed"]][["raw"]]

Compare example

note: definitely include because WP5 will do

Monetization

note: include definitely

Social analysis

note: if time allows

Other features

Check out the intro vignette for examples (to be added)

• correlated exposures

• life table analysis

• get_daly

Exporting results

note: show how to export csv, …

Export to Excel

openxlsx::write.xlsx(results_pm_copd, file = "../testing/Workshops_and_demos/WS_WP5/exported_results/results_pm_copd.xlsx")

Exported to .xlsx format

Export to .csv

TODO

Visualize

Out of scope of healthiar

• Using R, e.g. with ggplot2 package

• Using Excel (once results are exported)

Introduce homework

note: stress that they must install & test healthiar before 2nd WS

note: AC: not more than 2-3 exercises, one with provided numbers, one where they use exdat

note: put exercises in PDF on Teams

If you encounter challenges during installation, get in touch with us!

Outlook WS2

note: mention programme WS2 & explicitly YOU WILL HAVE TO PROGRAMME IN RSTUDIO USING HEALTHIAR

note: document any suggestions for improvements